WO2024060537A1 - 电池异常自放电预警方法、系统、电子设备及存储介质 - Google Patents

电池异常自放电预警方法、系统、电子设备及存储介质 Download PDF

Info

Publication number
WO2024060537A1
WO2024060537A1 PCT/CN2023/081831 CN2023081831W WO2024060537A1 WO 2024060537 A1 WO2024060537 A1 WO 2024060537A1 CN 2023081831 W CN2023081831 W CN 2023081831W WO 2024060537 A1 WO2024060537 A1 WO 2024060537A1
Authority
WO
WIPO (PCT)
Prior art keywords
self
cell
discharge
charge
state
Prior art date
Application number
PCT/CN2023/081831
Other languages
English (en)
French (fr)
Inventor
王勇士
谢晖
朱金鑫
黄敏
刘振勇
汪俊君
卢放
张书涛
刘凯
沈健
Original Assignee
岚图汽车科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 岚图汽车科技有限公司 filed Critical 岚图汽车科技有限公司
Priority to EP23764216.0A priority Critical patent/EP4369013A1/en
Publication of WO2024060537A1 publication Critical patent/WO2024060537A1/zh

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
    • B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present disclosure relates to the field of battery technology, and more specifically, to a battery abnormal self-discharge early warning method, system, electronic equipment and storage medium.
  • the battery management system (BMS, Battery Management System) is a control system that protects the safety of power batteries. It monitors the usage status of the battery at all times, alleviates the inconsistency of the battery pack through certain measures, and provides guarantee for the safety of new energy vehicles.
  • the power battery used in new energy vehicles is a battery system composed of multiple secondary batteries connected in series and parallel.
  • the battery has problems such as thermal runaway and poor consistency.
  • One of the main reasons for battery thermal runaway or battery consistency deterioration is battery self-discharge.
  • the main causes of battery self-discharge include micro-short circuit caused by external factors, micro-short circuit caused by internal factors of the battery, or energy consumption caused by intense side reactions inside the battery. Therefore, how to further effectively improve the accuracy of early warning of self-discharge in vehicle battery systems is an issue that needs to be solved urgently.
  • the present disclosure provides a battery abnormal self-discharge early warning method, system, electronic device and storage medium. By utilizing one or more embodiments of the present disclosure, it solves how to further effectively improve vehicle performance. The issue of the accuracy of early warning of self-discharge in battery systems.
  • a battery abnormal self-discharge early warning method including:
  • a battery safety warning is performed based on the judgment result.
  • a battery abnormal self-discharge early warning system including:
  • the status acquisition module is used to obtain multiple cell voltage extreme values, cell state of charge extreme values and phase values within the current working condition cycle of the vehicle battery system based on the working condition information, structural information and consistency information of the vehicle battery system. adjacent data time interval;
  • a data calculation module configured to calculate the self-discharge capacity, self-discharge energy and average self-discharge of the vehicle battery system based on the cell voltage extreme value, the cell state-of-charge extreme value and the adjacent data time interval. Discharge current value;
  • a system judgment module configured to judge whether self-discharge occurs in the vehicle battery system based on whether any of the self-discharge capacity, the self-discharge energy, and the average self-discharge current value is greater than a preset threshold;
  • a safety warning module is used to perform battery safety warning based on the judgment results.
  • an electronic device including a memory and a processor.
  • the processor is configured to implement an abnormal self-discharge warning for any battery in the first aspect when executing a computer management program stored in the memory. Method steps.
  • a computer-readable storage medium on which a computer management program is stored.
  • the computer management program is executed by a processor, any battery abnormality self-operation in the first aspect is realized. Steps of discharge warning method.
  • the present disclosure provides a battery abnormal self-discharge early warning method, system, electronic equipment and storage medium.
  • the method includes: based on the working condition information, structure information and consistency information of the vehicle battery system, obtaining the current working condition cycle of the vehicle battery system. There are multiple cell voltage extreme values, cell state of charge extreme values and adjacent data time intervals; based on the cell voltage extreme value, the cell state of charge extreme value and the adjacent data time interval, Calculate the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system; determine whether self-discharge occurs in the vehicle battery system based on the self-discharge capacity, the self-discharge energy and the average self-discharge current value. Discharge; perform battery safety warning based on the judgment results.
  • This disclosure calculates the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system based on the working condition information, structure information and consistency information of the vehicle battery system, and calculates the self-discharge capacity, self-discharge energy and/or The average self-discharge current value determines whether self-discharge occurs in the vehicle battery system, thereby achieving early warning of self-discharge in the vehicle battery system under different working conditions, different battery structures, and different consistency states of all single cells, thereby greatly improving It improves the safety of the vehicle battery system and the user experience of the passengers.
  • Figure 1 shows a flow chart of a battery abnormal self-discharge early warning method according to some embodiments of the present disclosure
  • Figure 2 shows a schematic structural diagram of a battery abnormal self-discharge early warning system according to some embodiments of the present disclosure
  • Figure 3 shows a schematic diagram of the hardware structure of an electronic device according to some embodiments of the present disclosure
  • FIG. 4 shows a schematic diagram of the hardware structure of a computer-readable storage medium according to some embodiments of the present disclosure.
  • Figure 1 is a flow chart of a battery abnormal self-discharge early warning method provided by the present disclosure. As shown in Figure 1, the method includes:
  • Step S100 Based on the working condition information, structure information and consistency information of the vehicle battery system, obtain multiple cell voltage extreme values, cell state of charge extreme values and adjacent data times within the current working condition period of the vehicle battery system. interval;
  • the execution subject of the method of this embodiment can be a computer terminal device with data processing, network communication and program running functions, such as a computer, a tablet computer, etc.; it can also be a server device with the same similar functions, or it can It is a cloud server with similar functions, and this embodiment does not limit this.
  • this embodiment and the following embodiments will be described using a server device as an example.
  • the working condition information may include the charging working condition, discharging working condition and resting working condition of the battery system
  • the structural information may be that a single battery cell constitutes a battery cell group and each battery cell group is connected in series or Multiple cells form a cell group and each cell group is connected in series.
  • the consistency information may be the consistency state of all individual cells in the battery system, including an idealized state and a non-idealized state.
  • the current working condition period may refer to obtaining corresponding data during the time when the battery system is in the current working condition.
  • the adjacent data time interval may refer to the collection time interval of the multiple cell voltage extreme values and the cell state of charge extreme value data, and all data can be obtained through the adjacent time intervals. the time interval between.
  • Step S200 Calculate the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system based on the cell voltage extreme value, the cell state-of-charge extreme value and the adjacent data time interval. ;
  • Step S300 judging whether the vehicle battery system has self-discharge according to whether any one of the self-discharge capacity, the self-discharge energy and the average self-discharge current value is greater than a preset threshold;
  • the preset threshold can be 0.
  • the user can select one or more of them as judgment conditions according to actual usage requirements, and this embodiment does not limit this.
  • Step S400 Perform battery safety warning based on the judgment result.
  • the present disclosure proposes a battery abnormality Self-discharge early warning method.
  • the method includes: based on the working condition information, structural information and consistency information of the vehicle battery system, obtaining multiple cell voltage extreme values, cell state of charge extreme values and adjacent data times within the current working condition period of the vehicle battery system.
  • Interval calculate the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system based on the cell voltage extreme value, the cell state-of-charge extreme value and the adjacent data time interval; Determine whether self-discharge occurs in the vehicle battery system based on whether any of the self-discharge capacity, the self-discharge energy and the average self-discharge current value is greater than a preset threshold; perform battery safety based on the judgment result Early warning.
  • This disclosure calculates the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system based on the working condition information, structure information and consistency information of the vehicle battery system, and calculates the self-discharge capacity, self-discharge energy and/or The average self-discharge current value determines whether self-discharge occurs in the vehicle battery system, thereby achieving early warning of self-discharge in the vehicle battery system under different working conditions, different battery structures, and different consistency states of all single cells, thereby greatly improving It improves the safety of the vehicle battery system and the user experience of the passengers.
  • the self-discharge capacity of the vehicle battery system is calculated based on the cell voltage extreme value, the cell state-of-charge extreme value and the adjacent data time interval.
  • the steps for self-discharge energy and average self-discharge current values include:
  • the consistency information is an idealized state
  • the structure information is single-parallel-multi-series or multiple-parallel-multi-serial, the first time interval between two charging ends is obtained
  • the idealized state is that the difference between the lowest voltage of the cell and the highest voltage of the cell after the previous charge is less than or equal to the preset difference
  • the extreme value of the state of charge of the cell includes the maximum value of the state of charge of the cell and the minimum value of the state of charge of the cell;
  • the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system are calculated according to the first extreme value difference, the rated capacity of the cell, the rated energy of the cell and the first time interval.
  • the BMS stores an established strategy for charging the battery.
  • the industry generally calls it the battery charging MAP table.
  • the charging rate mainly depends on the SOC (State of charge) of the battery. ) status (equivalent to battery voltage), battery temperature.
  • 100 cells as an example, if it is a single-parallel-multiple-string structure, all 100 cells are connected in series; if every two cells are connected in parallel to form a cell group, each cell group is connected in series, that is, 2 single cells are connected in parallel, forming a total of 50 cells. If five battery cells are connected in parallel to form a battery cell group, the battery cell groups are connected in series, forming a total of 20 battery cell groups, and the 20 battery cell groups are connected in series.
  • the highest voltage of the cell V maxN and the lowest voltage of the cell V minN at the normal end of the Nth charge can also be taken.
  • the state of charge of the corresponding monomer is calculated as SOC high N and SOC low respectively. N.
  • the self-discharge capacity (capacity) of the self-discharge battery cell is ( SOC high N -SOC low N )*x 1
  • the self-discharge energy is (SOC high N -SOC low N )*e 1
  • the average self-discharge current of the corresponding self-discharge cell during this period is (SOC high N -SOC low N )*x 1 /t N .
  • two cells in the module are connected in parallel to form a cell group, and each cell group is connected in series.
  • the consistency state of all individual cells in this battery system is The idealized state, that is, the capacity, internal resistance, polarization internal resistance, etc. are all consistent and are used as the analysis objects. Assume that the rated capacity of a single battery cell is x 1 and the rated energy is e 1 , then the rated capacity of a single string battery pack (2 cells in parallel) is 2x 1 and the rated energy is 2e 1 .
  • a cell has a self-discharge problem after the first charge, its power will be self-consumed while doing work externally, then V max2 > V min2 , and the corresponding SOC high 2 > SOC low 2.
  • the time interval between the two charge ends is t 1
  • the self-discharge power (capacity) of the self-discharge cell is (SOC high 2 - SOC low 2 ) * 2x 1
  • the self-discharge energy is (SOC high 2 - SOC low 2 ) * 2e 1
  • the corresponding average self-discharge current of the self-discharge cell in this time period is (SOC high 2 - SOC low 2 ) * 2x 1 /t 1 .
  • the maximum cell voltage V maxN and the minimum cell voltage V minN at the end of the Nth charge can also be taken, and the state of charge of the corresponding cell can be calculated as SOC high N and SOC low N respectively according to the OCV-SOC data.
  • the self-discharge power (capacity) of the self-discharge cell is (SOC high N -SOC low N )*2x 1
  • the self-discharge energy is (SOC high N -SOC low N )*2e 1
  • the average self-discharge current of the corresponding self-discharge cell during this period is (SOC high N -SOC low N )*2x 1 /t N .
  • multiple cells in the module are connected in parallel (assuming a cells are connected in parallel) to form a cell group, and each cell group is connected in series.
  • Each cell group (a cells are connected in parallel)
  • the rated capacity of parallel connection is ax 1 and the rated energy is ae 1 .
  • the results of the above five cases are multiplied by a times.
  • the result in the first possible application scenario should change to: (SOC high 2 -SOC low 2 )*ax 1
  • the BMS reports data to the cloud platform, including the highest cell voltage V max , the lowest cell voltage V min , and the average cell voltage V mea .
  • V mea is used instead of V max to improve the stability of the calculation.
  • the abnormal battery self-discharge early warning method further includes:
  • the working condition information is a charging end state
  • the structure information is a single-parallel-multi-serial state
  • the consistency information is a non-idealized state
  • a second time interval between two charging ends is obtained, wherein:
  • the non-idealized state is that the difference between the minimum voltage of the cell and the maximum voltage of the cell after the previous charge is greater than the preset difference;
  • the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system are calculated according to the second extreme value difference, the single cell rated capacity, the single cell rated energy and the second time interval.
  • the maximum voltage V max1 of the cell and the minimum voltage V min1 of the cell are calculated based on the OCV-SOC data.
  • the state of charge of the corresponding cell is calculated respectively. It is 1 for SOC high and 1 for low SOC. If the consistency of the battery system is not ideal at this time, then V max1 > V min1 , and the corresponding SOC high 1 > SOC low 1 .
  • the maximum voltage of the cell V max2 and the minimum voltage of the cell V min2 are calculated as SOC high 2 and SOC low 2 respectively. .
  • the corresponding SOC high 2 > SOC low 2 is calculated as SOC high 2 and SOC low 2 .
  • the self-discharge capacity (capacity) is (SOC low 1 -SOC low 2 )*x 1
  • the self-discharge energy is (SOC low 1 -SOC low 2 )*e 1
  • the average self-discharge current is (SOC low 1 -SOC low 2 )*x 1 /t 1 .
  • (SOC low 1 - SOC low 2 ) is the second extreme value
  • x 1 is the rated capacity of the unit
  • e 1 is the rated energy of the unit
  • t 1 is the second time interval.
  • the highest voltage of the cell V maxN and the lowest voltage of the cell V minN at the normal end of the Nth charge can also be taken.
  • the state of charge of the corresponding monomer is calculated as SOC high N and SOC low respectively. N.
  • V min1 > V minN the corresponding SOC low 1 > SOC low N
  • the battery core will have a self-discharge problem.
  • the self-discharge capacity (capacity) is (SOC low 1 -SOC low N )*x 1
  • the self-discharge energy is (SOC low 1 -SOC low N )*e 1
  • the average self-discharge current is (SOC low 1 -SOC low N )*x 1 /t N .
  • the polarization internal resistance of the battery is inconsistent.
  • the standing time can be 10 minutes, 20 minutes, 30 minutes, 60 minutes, 90 minutes, or 120 minutes.
  • the longer the standing time the better the depolarization effect of the battery core and the more accurate the self-discharge judgment of the battery core.
  • the smaller the probability of the data that can be obtained by the car in practical applications a trade-off should be made between the length of the resting time and the probability of the available values.
  • the charging cut-off voltage is constant, but due to the influence of charging polarization, the corresponding maximum voltage of the cell after standing is lower than the charging cut-off voltage.
  • the self-discharge capacity (capacity) of the self-discharge cell is (SOC low 1 - SOC low 2 )*x 1
  • the self-discharge energy is (SOC low 1 -SOC low 2 )*e 1
  • the average self-discharge current is (SOC low 1 -SOC low 2 )*x 1 /t 1 .
  • the self-discharge capacity (capacity) of the self-discharge cell is ((SOC low 1 - SOC low 2 ) - (SOC high 1 - SOC high 2) )*x 1
  • the self-discharge energy is ((SOC low 1 - SOC low 2 )-(SOC high 1 -SOC high 2 ))*e 1
  • the average self-discharge current is ((SOC low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*x1/t1.
  • the maximum cell voltage V maxN and the minimum cell voltage V minN at the end of the Nth charge can also be taken, and the charge state of the corresponding cell can be calculated as SOC high N and SOC low N respectively according to the OCV-SOC data.
  • the time interval between the two charge ends is t N .
  • the self-discharge power (capacity) of the self-discharge cell is ((SOC low 1 -SOC low N )-(SOC high 1 -SOC high N) )*x 1
  • the self-discharge energy is ((SOC low 1 -SOC low N )-(SOC high 1 -SOC high N ))*e 1
  • the average self-discharge current is ((SOC low 1 -SOC low N )-(SOC high 1 -SOC high N ))*x 1 /t 1 .
  • the self-discharge capacity, self-discharge energy, and self-discharge current of the battery calculated should be multiplied by the corresponding SOH.
  • the self-discharge status of the target vehicle can also be judged based on big data statistical technology.
  • big data statistical technology in order to improve the accuracy of data calculation and increase the value probability of available data, it is also possible not to Relying on the static depolarization process after charging, due to the fixed charging strategy of BMS, it enters the trickle charging mode at the end of charging, that is, charging at a fixed small rate for a period of time. Based on big data statistical technology, it is estimated that this type of battery has a certain cycle life.
  • the voltage drop amplitude b of the highest voltage cell caused by depolarization and the voltage drop of the lowest voltage cell are left for a certain period of time (which can range from 10 minutes to 120 minutes)
  • the voltage drop amplitude c of the body is substituted into the calculation formulas of the above situations to obtain the self-discharge capacity, self-discharge energy, and average self-discharge current.
  • the highest cell voltage is V max1
  • the lowest cell voltage is V min1
  • the equivalent depolarization highest cell voltage is (V max1 -b)
  • the equivalent The lowest cell voltage for depolarization is (V min1 -c).
  • the self-discharge capacity (capacity) of the self-discharge cell is ((SOC low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*x 1
  • the self-discharge energy is ((SOC low 1 -SOC Low 2 )-(SOC High 1 -SOC High 2 ))*e 1
  • the average self-discharge current is ((SOC Low 1 -SOC Low 2 )-(SOC High 1 -SOC High 2 ))*x 1 /t 1 .
  • the abnormal battery self-discharge early warning method further includes:
  • the consistency information is an idealized state
  • the structure information is a single-parallel-multiple-string, obtaining a third time interval between two charging completions
  • the SOC of the power battery will be reduced to a set value such as 20% before the driving charging mode is turned on. In this way, there will be a fixed low SOC state, which creates better conditions for comparing the low-end state of the battery and identifying self-discharge problems.
  • the data at the end of any two discharges can also be compared. Since lithium iron phosphate batteries have an obvious plateau period, the SOC error corresponding to the voltage calculation is large. It is recommended to give priority to the data in the interval below 30% for calculation.
  • the maximum cell voltage when the first discharge reaches the set SOC is V max1 , and the corresponding SOC state is SOC high 1 .
  • the minimum cell voltage V min1 when the first discharge reaches the set SOC, and the corresponding SOC state is SOC low 1 .
  • the above steps all use the maximum cell voltage as the reference object. If there is an average cell voltage, the average cell voltage V mea1 and the corresponding SOC mea1 are used as the reference object.
  • the maximum cell voltage V max2 and the minimum cell voltage V min2 when the SOC is discharged for the second time are set.
  • the corresponding SOC states are SOC high 2 and SOC low 2 , with the maximum cell voltage as the reference object.
  • the time interval after the end of two discharges is t 1 .
  • V max2 > V min2
  • the corresponding SOC high 2 SOC low 2 .
  • the self-discharge capacity is (SOC high 2 - SOC low 2 )*x 1
  • the self-discharge energy is (SOC high 2 - SOC low 2 )*e 1
  • the discharge current is (SOC high 2 -SOC low 2 )*x1/t1.
  • (SOC high 2 - SOC low 2 ) is the third extreme difference value
  • x 1 is the rated capacity of the unit
  • e 1 is the rated energy of the unit
  • t 1 is the third time interval.
  • the abnormal battery self-discharge early warning method further includes:
  • the working condition information is a discharge state
  • the consistency is an idealized state
  • the structure information is multiple parallel multiple series, obtain the fourth time interval at the end of two charges
  • the fourth extreme difference value is obtained based on the lowest voltage of the cell and the highest voltage of the cell after the previous charge
  • the fifth extreme difference value is obtained through the lowest voltage of the cell and the highest voltage of the cell after the last charge
  • the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system are calculated according to the sixth extreme value difference, the rated capacity of the cell, the rated energy of the cell and the fourth time interval.
  • the self-discharge capacity is ((SOC low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*x 1
  • the self-discharge energy is ((SOC Low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*e 1
  • the average self-discharge current is ((SOC low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*x 1 / ⁇ t 1 .
  • the fourth extreme difference value is (SOC low 1 - SOC low 2 )
  • the fifth extreme difference value is (SOC high 1 - SOC high 2 )
  • x 1 is the rated capacity of a single unit
  • e 1 is the rated energy of the unit
  • ⁇ t 1 is the fifth time interval.
  • the abnormal battery self-discharge early warning method further includes:
  • the seventh extreme difference value is obtained based on the lowest voltage of the cell and the highest voltage of the cell in the previous resting state
  • the eighth extreme difference value is obtained through the lowest voltage of the cell and the highest voltage of the cell in the last resting state
  • the difference between the eighth extreme difference value and the seventh extreme difference value is used as the ninth extreme difference value
  • the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system are calculated according to the ninth extreme value difference, the rated capacity of the cell, the rated energy of the cell and the fifth time interval.
  • the selection of static scenes and the application of static data have two advantages.
  • the vehicle is left stationary with low power consumption (such as current ⁇ 1/20C) for a long period of time, then take the time of stationary t1 (for better depolarization, generally t1 needs ⁇ 10 minutes, and the preset stationary time threshold is ten minutes).
  • the highest cell voltage V max1 and the lowest cell voltage V min1 correspond to SOC high 1 and SOC low 1 ; take the highest cell voltage V max2 and the lowest cell voltage V at time t 2 before the end of standing. min2 , the corresponding state of charge is SOC high 2 and SOC low 2 .
  • a cell has a self-discharge problem during this rest period, its self-discharge capacity is ((SOC high 2 - SOC low 2 ) - (SOC high 1 - SOC low 1 ))*x 1 , and the self-discharge energy is ((SOC high 2 -SOC low 2 )-(SOC high 1 -SOC low 1 ))*e 1 , the average self-discharge current is ((SOC high 2 -SOC low 2 )-(SOC high 1 -SOC low 1 ) ))*x 1 /(t 2 -t 1 ).
  • the eighth extreme difference value is (SOC High 2 - SOC low 2 )
  • the seventh extreme difference value is (SOC high 1 - SOC low 1 )
  • the ninth extreme difference value is ((SOC high 2 -SOC low 2 ) - (SOC high 1 -SOC low) 1 ))
  • x 1 is the rated capacity of the unit
  • e 1 is the rated energy of the unit
  • (t 2 -t 1 ) is the fifth time interval.
  • the abnormal battery self-discharge early warning method further includes:
  • Step S500 Obtain the voltage drop amplitude or voltage recovery degree of the single voltage extreme value of the vehicle for different service years, different charging rates, and different power-free storage periods, and determine based on the voltage drop amplitude or voltage recovery degree. Whether self-discharge occurs in the vehicle battery system.
  • the vehicle is suddenly powered off from a high-rate charging condition and enters a state of no power consumption. After a period of time t1, the vehicle is powered on again.
  • the highest cell voltage before the vehicle is powered off is V max1 and the lowest cell voltage is V min1 ;
  • the highest cell voltage after the vehicle is powered back on is V max2 and the lowest cell voltage is V min2 . Since the vehicle is charged at a high rate before being powered off, the battery is polarized and the vehicle is depolarized after being left standing for a period of time, so V max1 > V max2 and V min1 > V min2 .
  • the voltage drop amplitude of the highest voltage cell and the lowest voltage cell with different service years (number of cycles), different charging rates, and different no-power consumption storage periods (depolarization time) is calculated. If a battery system is found If the voltage drop of the lowest voltage becomes significantly larger after a certain period of rest, it indicates that the battery cell has a self-discharge problem.
  • Example 1 Big data statistics of the batteries of 10,000 vehicles after 2 years of use, with a cycle of 100 cycles, were powered off and entered a no-power state at a 1C charging rate. After leaving it alone for 60 minutes, the highest cell voltage dropped according to statistics a volts, the lowest cell voltage drops by b volts. However, for the same battery under the same charging and resting conditions, the highest cell voltage drops by a volt and the lowest cell voltage drops by (b+c) volts, which means that the lowest voltage cell has a self-discharge problem.
  • Example 2 Big data statistics show that a battery system of a vehicle has been used for 2 years and has a cycle of 100 weeks. When it is powered off at a 1C charging rate and enters a power-free state, the highest cell voltage drops by a volt and the lowest cell voltage drops by b volts after standing for 60 minutes. However, after standing for 60 minutes under the same working conditions, the highest cell voltage drops by a volt and the lowest cell voltage drops by (b+c) volts. This indicates that the lowest voltage cell has a self-discharge problem.
  • the SOC corresponding to the voltage drop of c volts is obtained by checking the OCV-SOC data table.
  • the self-discharge capacity is SOC*x 1
  • the self-discharge capacity is SOC*e 1
  • the self-discharge current is SOC*x1/60 minutes.
  • the vehicle is suddenly powered off from a high-rate discharge condition and enters a state of no power consumption. After a period of time t 1 , the vehicle is powered on again.
  • the highest cell voltage before the vehicle is powered off is V max1 and the lowest cell voltage is V min1 ;
  • the highest cell voltage after the vehicle is powered back on is V max2 and the lowest cell voltage is V min2 . Since the vehicle is discharged at a high rate before the vehicle is powered off, the battery is polarized and the vehicle is depolarized after standing still for a period of time, so V max1 ⁇ V max2 and V min1 ⁇ V min2 .
  • the vehicle enters a power-off and no-power consumption state under any operating conditions, and then wakes up for monitoring regularly. Generally, it wakes up for a few minutes.
  • the power consumption in the wake-up state is very small, and usually the current is only a few tenths of an amp. It can be treated as a no-power consumption state.
  • This situation is similar to the laboratory testing of battery self-discharge. Detect the highest cell voltage and the lowest cell voltage at regular intervals and analyze the voltage drop trend. Theoretically, all single cells have weak self-discharge. Based on experience, within a short period of time, such as within 24 hours, the highest cell voltage remains unchanged.
  • the lowest cell voltage drop will show an obvious downward trend. Use this to determine the abnormal self-discharge problem of the battery, and calculate the corresponding self-discharge capacity, self-discharge energy, and self-discharge current.
  • a battery After a battery is powered off, it wakes up at a time interval of a hour (a is usually greater than 1), each wake-up lasts b hours (b is very small relative to a), and the highest cell voltage V max1 when it wakes up for the first time.
  • the lowest cell voltage V min1 corresponds to the battery state of charge: SOC high 1 and SOC low 1.
  • SOC high 1 and SOC low 1 At this time, since it has been standing for an hour, it can be considered that the battery has been well depolarized.
  • the highest cell voltage V max2 and the lowest cell voltage V min2 are corresponding to the battery state of charge: SOC high 2 and SOC low 2 .
  • the self-discharge capacity of the battery is ((SOC low 1 - SOC low 2 ) - (SOC high 1 - SOC high 2 ))*x 1
  • the self-discharge energy is ((SOC low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*x 1
  • the average self-discharge current is ((SOC low 1 -SOC low 2 )-(SOC high 1 -SOC high 2 ))*x 1 /a.
  • the battery system will not have obvious attenuation problems in a short period of time, if the charging capacity or discharging capacity corresponding to the SOC shown on the display has an obvious trend of becoming smaller in a short period of time, it means that the battery has obvious self-discharge problems.
  • This method should take advantage of the big data of the cloud platform, conduct comparative analysis on all vehicles and batteries of the same model, same age, same number of cycles, and same SOH, and find out the average charge or discharge corresponding to the unit SOC change. power. Under normal circumstances, BMS will report the charging power to the cloud platform. The cloud platform can also calculate charging or discharging power based on ampere-hour points.
  • the charging or discharging capacity corresponding to the historical unit SOC of a certain car can also be compared longitudinally. If there is an obvious decrease in the amount in a short period of time (such as within 3 months), it can also be determined.
  • the car's battery has obvious self-discharge problems.
  • the charging power (capacity) corresponding to unit SOC (1% SOC) of a car during the first observed charging process is 1Ah (assuming the rated capacity is 100Ah and the rated energy is e), during the observation
  • self-discharge problem detection can also be performed based on voltage list data.
  • the voltage list can be data uploaded to the cloud platform by a manufacturer. Based on the analysis methods in the above embodiments, the voltage list data can be used to detect self-discharge problems.
  • the self-discharge of each single cell is distributed and calculated to identify the cell with the largest self-discharge current and provide early warning, even if it is not the cell with the lowest voltage.
  • Example 1 Corresponding to the thirteenth situation mentioned above, based on the voltage list: the vehicle is suddenly powered off from a high-rate discharge condition and enters a state of no power consumption. After a period of time t 1 , the vehicle is powered on again. The highest cell voltage before the vehicle is powered off is V max1 and the lowest cell voltage is V min1 ; the highest cell voltage after the vehicle is powered on again is V max2 and the lowest cell voltage is V min2 . Since the vehicle is discharged at a high rate before the vehicle is powered off, the battery is polarized and the vehicle is depolarized after standing still for a period of time, so V max1 ⁇ V max2 and V min1 ⁇ V min2 .
  • the recovery voltage of most cells is a, and the recovery voltage of one or several single cells is significantly smaller than a, it means that these cells have self-discharge problems or their polarization voltage is very small (equivalent Because the polarization internal resistance is very small). Compared with the identification of self-discharge mentioned above, this method can further prove that the battery core has self-discharge problem.
  • Example 2 Corresponding to the fourth situation mentioned above: all cell voltages after the first charge are V 11 /V 12 /V 13 ...V 1N , the average cell voltage is V 1mea , and the corresponding battery state of charge They are SOC 11 /SOC 12 /SOC 13 .... SOC 1N /SOC 1mea ; after the second charge, all cell voltages are V 21 /V 22 /V 23 ...V 2N , the average cell voltage is V 2mea , and the corresponding battery states of charge are SOC 21 /SOC 22 /SOC 23 .... SOC 2N /SOC 2mea ; The time interval between two charges is t 1.
  • the corresponding self-discharge current is calculated respectively.
  • the self-discharge capacity of the first battery cell is ((SOC 11 -SOC 21 )-(SOC 1mea -SOC 2mea ))*x 1
  • the self-discharge energy is ((SOC 11 -SOC 21 )-(SOC 1mea -SOC 2mea ))*e 1
  • the average self-discharge current is ((SOC 11 -SOC 21 )-(SOC 1mea -SOC 2mea ))*x 1 /t 1 .
  • the self-discharge problem of the target vehicle battery can also be judged and the thermal runaway risk can be evaluated based on the battery cell temperature sensor.
  • the battery temperature will approach the air temperature, and the temperature difference caused by the uneven charging and discharging and thermal management system of the battery will tend to become smaller.
  • the self-discharge current and temperature are used as evaluation methods for the risk of thermal runaway of the battery. The larger the self-discharge current and the higher the static temperature, the greater the risk of thermal runaway of the battery. This method requires that the battery system has a temperature sensor on each battery cell to improve the accuracy of temperature recognition.
  • This disclosure calculates the self-discharge capacity, self-discharge energy and average self-discharge power of the vehicle battery system from different working conditions, different battery structures and different consistency information of the vehicle battery system. current value, and determine whether self-discharge occurs in the vehicle battery system based on self-discharge capacity, self-discharge energy and/or average self-discharge current value, thereby achieving the consistency state of all individual cells under different working conditions, different battery structures, and different It provides early warning of self-discharge of the vehicle battery system, thereby greatly improving the safety of the vehicle battery system and the user experience of the passengers.
  • FIG. 2 is a schematic structural diagram of a battery abnormal self-discharge early warning system provided by an embodiment of the present disclosure.
  • a battery abnormal self-discharge early warning system includes a status acquisition module 100 and a data calculation module. 200.
  • the status acquisition module 100 is used to obtain multiple cell voltage extreme values, cell state of charge extreme values, and Adjacent data time interval; the data calculation module 200 is used to calculate the self-discharge capacity of the vehicle battery system based on the cell voltage extreme value, the cell state-of-charge extreme value and the adjacent data time interval. , self-discharge energy and average self-discharge current value; the system judgment module 300 is used to determine whether any one of the self-discharge capacity, the self-discharge energy and the average self-discharge current value is greater than a preset threshold. Whether self-discharge occurs in the vehicle battery system; the safety warning module 400 is used to perform battery safety warning based on the judgment result.
  • the battery abnormal self-discharge early warning system provided by the present disclosure corresponds to the battery abnormal self-discharge early warning method provided by the aforementioned embodiments.
  • the battery abnormal self-discharge early warning system please refer to Battery Abnormal Self-Discharge Early Warning The relevant technical features of the method will not be described again here.
  • FIG. 3 is a schematic diagram of an electronic device provided by an embodiment of the present disclosure.
  • the embodiment of the present disclosure provides an electronic device, including a memory 1310, a processor 1320, and a computer program 1311 stored on the memory 1310 and executable on the processor 1320.
  • the processor 1320 executes the computer program 1311 follows these steps:
  • the vehicle battery system Based on the working condition information, structural information and consistency information of the vehicle battery system, obtain multiple cell voltage extreme values, cell state of charge extreme values and adjacent data time intervals within the current working condition cycle of the vehicle battery system; based on The self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system are calculated based on the cell voltage extreme value, the cell state-of-charge extreme value and the adjacent data time interval; according to Whether any of the self-discharge capacity, the self-discharge energy and the average self-discharge current value is greater than a preset threshold is used to determine whether self-discharge occurs in the vehicle battery system; a battery safety warning is performed based on the determination result.
  • FIG4 is a schematic diagram of an embodiment of a computer-readable storage medium provided by the present disclosure. As shown in FIG4 , this embodiment provides a computer-readable storage medium 1400 on which a computer program 1411 is stored. When the computer program 1411 is executed by a processor, the following steps are implemented:
  • multiple single cell voltage extreme values, single cell charge state extreme values and adjacent single cell voltage extreme values in the current working condition cycle of the vehicle battery system are obtained.
  • data time interval based on the single cell voltage extreme value, the single cell charge state extreme value and the adjacent data time interval, calculating the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system; judging whether the vehicle battery system has self-discharge according to the self-discharge capacity, the self-discharge energy and the average self-discharge current value; and issuing a battery safety warning based on the judgment result.
  • the present disclosure provides a battery abnormal self-discharge early warning method, system, electronic equipment and storage medium.
  • the method includes: based on the working condition information, structure information and consistency information of the vehicle battery system, obtaining the current working condition cycle of the vehicle battery system.
  • the threshold is used to determine whether self-discharge occurs in the vehicle battery system; a battery safety warning is performed based on the determination result.
  • This disclosure calculates the self-discharge capacity, self-discharge energy and average self-discharge current value of the vehicle battery system based on the working condition information, structure information and consistency information of the vehicle battery system, and calculates the self-discharge capacity, self-discharge energy and/or The average self-discharge current value determines whether self-discharge occurs in the vehicle battery system, thereby achieving early warning of self-discharge in the vehicle battery system under different working conditions, different battery structures, and different consistency states of all single cells, thereby greatly improving It improves the safety of the vehicle battery system and the user experience of the passengers.
  • embodiments of the present disclosure may be provided as methods, systems, or computer program products. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements one of the flowcharts.
  • a process or processes and/or a block diagram specifies functionality in one or more blocks.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device.
  • Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

本公开提供的一种电池异常自放电预警方法、系统、电子设备及存储介质，方法包括：基于车辆电池系统的工况信息、结构信息和一致性信息，获取车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；基于单体电压极值、单体荷电状态极值和相邻数据时间间隔，计算车辆电池系统的自放电容量、自放电能量和平均自放电电流值；根据自放电容量、自放电能量和平均自放电电流值中的任一项是否大于预设阈值以判断车辆电池系统是否出现自放电；基于判断结果进行电池安全预警。

Description

电池异常自放电预警方法、系统、电子设备及存储介质
相关申请的交叉引用
本公开要求于2022年9月21日提交、申请号为202211154244.6且名称为“电池异常自放电预警方法、系统、电子设备及存储介质”的中国专利申请的优先权，其全部内容通过引用合并于此。
技术领域
本公开涉及电池技术领域，更具体地，涉及一种电池异常自放电预警方法、系统、电子设备及存储介质。
背景技术
电池管理系统(BMS，Battery Management System)为一套保护动力电池使用安全的控制系统，时刻监控电池的使用状态，通过一定措施缓解电池组的不一致性，为新能源车辆的使用安全提供保障。
新能源汽车所用的动力电池是多个二次电池串并联之后组成的电池系统，电池有热失控、一致性变差等问题。而电池热失控或电池一致性变差的主要原因之一是电池的自放电。电池的自放电的主要原因包括外部因素引发的微短路、电池内部因素引起的微短路或电池内部副反应激烈导致的能量消耗。因此，如何进一步有效的提高车辆电池系统自放电情况的发生预警的精准程度是亟待解决的问题。
发明内容
本公开针对现有技术中存在的技术问题，提供一种电池异常自放电预警方法、系统、电子设备及存储介质，通过利用本公开内容的一个或多个实施方式解决了如何进一步有效的提高车辆电池系统自放电情况的发生预警的精准程度的问题。
根据本公开的第一方面，提供了一种电池异常自放电预警方法，包括：
基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；
基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；
根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；
基于所述判断结果进行电池安全预警。
根据本公开的第二方面，提供一种电池异常自放电预警系统，包括：
状态获取模块，用于基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；
数据计算模块，用于基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；
系统判断模块，用于根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；
安全预警模块，用于基于所述判断结果进行电池安全预警。
根据本公开的第三方面，提供了一种电子设备，包括存储器、处理器，所述处理器用于执行存储器中存储的计算机管理类程序时实现所述第一方面中任一电池异常自放电预警方法的步骤。
根据本公开的第四方面，提供了一种计算机可读存储介质，其上存储有计算机管理类程序，所述计算机管理类程序被处理器执行时实现所述第一方面中任一电池异常自放电预警方法的步骤。
本公开提供的一种电池异常自放电预警方法、系统、电子设备及存储介质，方法包括：基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；根据所述自放电容量、所述自放电能量和所述平均自放电电流值判断所述车辆电池系统是否出现自放电；基于所述判断结果进行电池安全预警。本公开通过基于车辆电池系统的工况信息、结构信息和一致性信息，计算车辆电池系统的自放电容量、自放电能量和平均自放电电流值，并基于自放电容量、自放电能量和/或平均自放电电流值判断车辆电池系统是否出现自放电，从而实现了在不同工况、不同电池结构以及不同所有单体电芯一致性状态下，车辆电池系统自放电情况的预警，进而大大的提高了车辆电池系统的安全性，以及乘驾人员的用户体验。
附图说明
图1示出了依据本公开一些实施例的电池异常自放电预警方法流程图；
图2示出了依据本公开一些实施例的电池异常自放电预警系统结构示意图；
图3示出了依据本公开一些实施例的电子设备的硬件结构示意图；
图4示出了依据本公开一些实施例的计算机可读存储介质的硬件结构示意图。
具体实施方式
下面结合附图和实施例，对本公开的具体实施方式作进一步详细描述。以下实施例用于说明本公开的内容，但不用来限制本公开的范围。
图1为本公开提供的一种电池异常自放电预警方法流程图，如图1所示，方法包括：
步骤S100：基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；
需要说明的是，本实施例方法的执行主体可以是具有数据处理、网络通信及程序运行功能的计算机终端设备，例如：电脑、平板电脑等；也可以是具有相同相似功能的服务器设备，还可以是具有相似功能的云服务器，本实施例对此不做限制。为了便于理解，本实施例及下述各实施例将以服务器设备为例进行说明。
可以理解的是，所述工况信息可以包括电池系统的充电工况、放电工况以及静置工况，所述结构信息可以是单个电芯组成电芯组且各个电芯组之间串联或多个电芯组成电芯组且各个电芯组之间串联，所述一致性信息可以是电池系统中所有单体电芯的一致性状态包括理想化状态和非理想化状态。
应理解的是，所述当前工况周期可以是指在电池系统处于当前工况状态时间内，获取对应的数据。
还可以理解的是，所述相邻数据时间间隔，可以是指所述多个单体电压极值和单体荷电状态极值数据的采集时间间隔，通过相邻时间间隔即可获取所有数据之间的时间间隔。
步骤S200：基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；
步骤S300：根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；
可以理解的是，所述自放电容量、自放电能量和平均自放电电流值任意一项大于预设阈值的情况下，都可以表示当前电池系统出现了自放电情况，预设阈值可以取0，用户可以根据实际使用需求任选其一或多个作为判断条件，本实施例对此不作限制。
步骤S400：基于所述判断结果进行电池安全预警。
可以理解的是，基于背景技术中的缺陷，本公开提出了一种电池异常 自放电预警方法。方法包括：基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；基于所述判断结果进行电池安全预警。本公开通过基于车辆电池系统的工况信息、结构信息和一致性信息，计算车辆电池系统的自放电容量、自放电能量和平均自放电电流值，并基于自放电容量、自放电能量和/或平均自放电电流值判断车辆电池系统是否出现自放电，从而实现了在不同工况、不同电池结构以及不同所有单体电芯一致性状态下，车辆电池系统自放电情况的预警，进而大大的提高了车辆电池系统的安全性，以及乘驾人员的用户体验。
在一种可能的实施例方式中，所述基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值的步骤，包括：
在所述工况信息为充电结束状态、所述一致性信息为理想化状态以及所述结构信息为单并多串或多并多串的情况下，获取两次充电结束的第一时间间隔，其中，所述理想化状态为前一次充电结束后的单体最低电压和单体最高电压之间的差值小于或等于预设差值；
获取后一次充电结束后的单体荷电状态极值，其中，所述单体荷电状态极值包括单体荷电状态极大值和单体荷电状态极小值；
基于所述后一次充电结束后单体荷电状态极大值和单体荷电状态极小值获取第一极值差；
根据所述第一极值差、单体额定容量、单体额定能量和所述第一时间间隔计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
在具体实现中，对于正常运行的新能源车辆，BMS存储有对电池的充电既定的策略，行业一般称之为电池充电MAP表，充电倍率主要取决于电池的SOC(State of charge，荷电状态)状态(等效于电池电压)、电池的温度。
取结构形式最简单的“单并多串”电池系统，此电池系统的所有单体电芯的一致性状态为理想化状态，即容量、内阻、极化内阻等均一致，作为分析对象。假设单体电芯的额定容量是x1，额定能量是e1。需要说明的是，单并多串即为所有的电芯都是串联连接的，而多并多串是是预设数量的并联形成电芯组，电芯组之间串联形成电池系统。以100个电芯为例，如果是单并多串结构则所有100个电芯都是串联连接；如果是每两个电芯并联为一个电芯组，各个电芯组之间串联，即2个单体电芯并联，总共形成50个电芯 组，50个电芯组之间采用串联方式连接；如果是每五个电芯并联为一个电电芯组，各个电芯组之间串联，总共形成20个电芯组，20个电芯组之间采用串联方式连接。
在一种可能的应用场景中，取第一次充电正常结束(充电电压达到截止电压)时的单体最高电压Vmax1、单体最低电压Vmin1，根据OCV(Open circuit voltage，开路电压)-SOC数据，计算对应单体的荷电状态分别为SOC高1、SOC低1。由于假定的分析对象是一个所有单体电芯一致性状态为理想化状态的电池系统，所以Vmax1＝Vmin1，对应的SOC高1＝SOC低1。
取第二次充电正常结束(充电电压达到截止电压)时的单体最高电压Vmax2、单体最低电压Vmin2，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高2、SOC低2。
如果在第一次充电结束之后，某电芯出现了自放电问题，则它的电量在对外做功的同时有一个自损耗，则Vmax2＞Vmin2，对应的SOC高2＞SOC低2。两次充电结束时间间隔为t1，则两次充电结束时间段内，自放电电芯自放电的电量(容量)为(SOC高2-SOC低2)*x1，自放电的能量为(SOC高2-SOC低 2)*e1，对应的自放电电芯在此时间段的平均自放电电流为(SOC高2-SOC低2)*x1/t1。需要说明的是，在本实施例中(SOC高2-SOC低2)为第一极差值，x1为单体额定容量，e1为单体额定能量，t1为第一时间间隔。
相应的，也可以取第N次充电正常结束时的单体最高电压VmaxN、单体最低电压VminN，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高N、SOC低N。假如从第一次充电结束至第N次充电结束时间段内(tN)，某电芯发生了自放电，则在此时间段内，此自放电电芯的自放电电量(容量)为(SOC高N-SOC低N)*x1，自放电的能量为(SOC高N-SOC低N)*e1，对应的自放电电芯在此时间段的平均自放电电流为(SOC高N-SOC低N)*x1/tN。
在另一种可能的应用场景中，取模组内两个电芯并联为一个电芯组，各个电芯组之间串联的电池系统，此电池系统的所有单体电芯的一致性状态为理想化状态，即容量、内阻、极化内阻等均一致，作为分析对象。假设单体电芯的额定容量是x1，额定能量是e1，则单串电池组(2个电芯并联)的额定容量是2x1，额定能量是2e1。
对应的第一种可能的应用场景中，假定此两个电芯并联为一个电芯组，各个电芯组之间串联的电池系统是一致性理想状态的，
取第一次充电正常结束(充电电压达到截止电压)时的单体最高电压Vmax1、单体最低电压Vmin1，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高1、SOC低1。由于假定的分析对象是一个所有单体电芯一致性状态为理想化状态的电池系统，所以Vmax1＝Vmin1，对应的SOC高1＝SOC低1。
取第二次充电正常结束(充电电压达到截止电压)时的单体最高电压Vmax2、单体最低电压Vmin2，根据OCV-SOC数据，计算对应单体的荷电状态 分别为SOC高2、SOC低2。
如果在第一次充电结束之后，某电芯出现了自放电问题，则它的电量在对外做功的同时有一个自损耗，则Vmax2＞Vmin2，对应的SOC高2＞SOC低2。两次充电结束时间间隔为t1，则两次充电结束时间段内，自放电电芯自放电的电量(容量)为(SOC高2-SOC低2)*2x1，自放电的能量为(SOC高2-SOC低2)*2e1，对应的自放电电芯在此时间段的平均自放电电流为(SOC高2-SOC低2)*2x1/t1。
相应的，也可以取第N次充电正常结束时的单体最高电压VmaxN、单体最低电压VminN，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高N、SOC低N。假如从第一次充电结束至第N次充电结束时间段内(tN)，某电芯发生了自放电，则在此时间段内，此自放电电芯的自放电电量(容量)为(SOC高N-SOC低N)*2x1，自放电的能量为(SOC高N-SOC低N)*2e1，对应的自放电电芯在此时间段的平均自放电电流为(SOC高N-SOC低N)*2x1/tN。
在另一种可能的应用场景中，模组内多个电芯并联(假定a个电芯并联)为一个电芯组，各个电芯组之间串联，每个电芯组(a个电芯并联)的额定容量是ax1，额定能量是ae1。以上五种情况的结果均乘以a倍。与第一种可能的应用场景中的结果应变化为：(SOC高2-SOC低2)*ax1
在另一种可能的应用场景中，BMS上报云平台的数据，除了有单体最高电压Vmax、单体最低电压Vmin，还有平均单体电压Vmea。则以上其中情况的计算均用Vmea替代Vmax，以提高计算的稳定性。
在一种可能的实施例方式中，所述电池异常自放电预警方法，还包括：
在所述工况信息为充电结束状态、所述结构信息为单并多串和所述一致性信息为非理想化状态的情况下，获取两次充电结束的第二时间间隔，其中，所述非理想化状态为前一次充电结束后的单体最低电压和单体最高电压之间的差值大于预设差值；
获取前一次充电结束后和后一次充电结束后单体荷电状态极小值的差值为第二极差值；
根据所述第二极值差、单体额定容量、单体额定能量和所述第二时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
在一种可能的应用场景中，在观察到的第一次充电正常结束之后，单体最高电压Vmax1、单体最低电压Vmin1，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高1、SOC低1。若此时电池系统一致性非理想状态，则Vmax1＞Vmin1，对应的SOC高1＞SOC低1。
在可观察到的第二次充电正常结束之后，单体最高电压Vmax2、单体最低电压Vmin2，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高 2、SOC低2。Vmax2＞Vmin2，对应的SOC高2＞SOC低2。
两次充电结束时间间隔为t1。因为BMS充电策略控制了充电截止电压，原则上Vmax1＝Vmax2，对应的SOC高1＝SOC高2。
如果Vmin1＝Vmin2，对应的SOC低1＝SOC低2，则t1时间内，没有电芯出现自放电问题。
如果Vmin1＞Vmin2，对应的SOC低1＞SOC低2，则t1时间内，电芯出现自放电问题。自放电电量(容量)为(SOC低1-SOC低2)*x1，自放电能量为(SOC低1-SOC低2)*e1，平均自放电电流为(SOC低1-SOC低2)*x1/t1。需要说明的是，在本实施例中(SOC低1-SOC低2)为第二极差值，x1为单体额定容量，e1为单体额定能量，t1为第二时间间隔。
相应的，也可以取第N次充电正常结束时的单体最高电压VmaxN、单体最低电压VminN，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高N、SOC低N。两次充电结束时间间隔为tN。因为BMS充电策略控制了充电截止电压，原则上Vmax1＝VmaxN，对应的SOC高1＝SOC高N。
如果Vmin1＝VminN，对应的SOC低1＝SOC低N，则tN时间内，没有电芯出现自放电问题。
如果Vmin1＞VminN，对应的SOC低1＞SOC低N，则tN时间内，电芯出现自放电问题。自放电电量(容量)为(SOC低1-SOC低N)*x1，自放电能量为(SOC低1-SOC低N)*e1，平均自放电电流为(SOC低1-SOC低N)*x1/tN。
在另一种可能的实施例中，电池的极化内阻不一致，为消除充电极化带来的电池SOC估算影响，每次都取充电正常结束后静置一段时间之后的电压值。静置时间可以取10分钟、20分钟、30分钟、60分钟、90分钟、120分钟，一般的，静置时间越长电芯去极化效果越好电芯的自放电判断越准，但是实车可取到的数据概率越小，实际应用中应对静置时间长度和可取值的概率做一个取舍。
取第一次充电结束静置一段时间之后的单体最高电压Vmax1、单体最低电压Vmin1，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高1、SOC低1。
取第二次充电结束静置一段时间之后的单体最高电压Vmax2、单体最低电压Vmin2，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高2、SOC低2。两次充电结束时间间隔为t1。
根据BMS充电策，充电截止电压恒定，但是由于充电极化影响，对应的静置之后的单体最高电压较充电截止电压较低。
假定Vmax1＝Vmax2，相应的SOC高1＝SOC高2，自放电电芯的自放电电量(容量)为(SOC低1-SOC低2)*x1，自放电能量为(SOC低1-SOC低2)*e1，平均自放电电流为(SOC低1-SOC低2)*x1/t1。
在另一种可能的实施例中，进一步的，为消除极化等不确定因素的影响(假定Vmax1≠Vmax2，相应的SOC高1≠SOC高2)，以最高单体电压为参考 对象，则自放电电芯的自放电电量(容量)为((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1，自放电能量为((SOC低1-SOC低2)-(SOC高1-SOC高2))*e1，平均自放电电流为((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1/t1。
进一步的，也可以取第N次充电正常结束时的单体最高电压VmaxN、单体最低电压VminN，根据OCV-SOC数据，计算对应单体的荷电状态分别为SOC高N、SOC低N。两次充电结束时间间隔为tN。为消除极化的影响(假定Vmax1≠VmaxN，相应的SOC高1≠SOC高N)，以最高单体电压为参考对象，则自放电电芯的自放电电量(容量)为((SOC低1-SOC低N)-(SOC高1-SOC高 N))*x1，自放电能量为((SOC低1-SOC低N)-(SOC高1-SOC高N))*e1，平均自放电电流为((SOC低1-SOC低N)-(SOC高1-SOC高N))*x1/t1。
同时，随着电池的使用，电池会发生相应的衰减，则其容量保持率/健康状态SOH(state of health，电池健康度)则由新电池的100％下降至某个数值。则以上两种情况计算的电池的自放电电量、自放电能量、自放电电流均应乘以相应的SOH。
在一种可能的实施例中，还可以基于大数据统计技术，对目标车辆进行自放电情况判断，在本实施例中，为提高数据计算的精度、提高可用数据的取值概率，也可以不依赖充电后的静置去极化过程，由于BMS固定的充电策略，在充电末期进入涓流充电模式即固定的小倍率充电一段时间，基于大数据统计技术，统计此型号电池使用到一定循环寿命周期时在此涓流充电模式结束后静置一定时间(可以是10分钟至120分钟不等，分别进行统计)之后去极化带来的最高电压单体的电压压降幅度b、最低电压单体的电压压降幅度c，代入上面几种情况的计算公式，得到自放电容量、自放电能量、平均自放电电流。以第四种情况为例，第一次充电结束时，最高单体电压为Vmax1，最低单体电压为Vmin1，等效去极化最高单体电压为(Vmax1-b)，等效去极化最低单体电压为(Vmin1-c),依此查OCV-SOC数据表得出相应的SOC高1、SOC低1；第二次充电结束时，最高单体电压为Vmax2，最低单体电压为Vmin2，等效去极化最高单体电压为，等效去极化最低单体电压为，依此查OCV-SOC数据表，得出相应的SOC高2、SOC低2；则自放电电芯的自放电电量(容量)为((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1，自放电能量为((SOC低1-SOC低2)-(SOC高1-SOC高2))*e1，平均自放电电流为((SOC低 1-SOC低2)-(SOC高1-SOC高2))*x1/t1。
在一种可能的实施例方式中，所述电池异常自放电预警方法，还包括：
在所述工况信息为放电状态、所述一致性信息为理想化状态且所述结构信息为单并多串的情况下，获取两次充电结束的第三时间间隔；
获取后一次充电结束后的单体荷电状态极值；
基于所述后一次充电结束后单体荷电状态极大值和单体荷电状态极小值获取第三极值差；
根据所述第三极值差、单体额定容量、单体额定能量和所述第三时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
在一种可能的应用场景中，尤其对于增程式车辆或混合动力车辆，动力电池的SOC会降低至设定值如20％，才会开启行车充电模式。这样就会有一个固定的低SOC状态，为电池的低端状态对比、识别自放电问题创造了较好的条件。当然，也可以以任意两次放电结束的数据做对比，由于磷酸铁锂电池有明显的平台期，根据电压计算对应的SOC误差大，建议优先选择SOC在30％以下的区间数据进行计算。
第一次放电至设定SOC时的最大单体电压为Vmax1，对应的SOC状态为SOC高1。第一次放电至设定SOC时的最小单体电压Vmin1，对应的SOC状态为SOC低1。上述步骤均以最大单体电压最为参考对象，如果有平均单体电压，则优先用平均单体电压Vmea1和对应的SOCmea1作为参考对象。
第二次放电至设定SOC时的最大单体电压Vmax2,最小单体电压Vmin2,对应的SOC状态为SOC高2、SOC低2，以最大单体电压最为参考对象。两次放电结束之后的时间间隔为t1。
在电池系统为理想电池的情况下，第一次放电结束时Vmax1＝Vmin1，相应的SOC高1＝SOC低1。如果在第一次放电结束之后，第二次放电结束之前，电池发生了自放电问题，则Vmax2＞Vmin2,相应的SOC高2＝SOC低2。则在这两次放电结束之间的时间段内，自放电容量为(SOC高2-SOC低2)*x1，自放电能量为(SOC高2-SOC低2)*e1，平均自放电电流为(SOC高2-SOC低2)*x1/t1。需要说明的是，在本实施例中(SOC高2-SOC低2)为第三极差值，x1为单体额定容量，e1为单体额定能量，t1为第三时间间隔。
进一步的，如果是模组内a个电芯并联为电芯组、每个电芯组之间采用串联方式结构的电池系统，其自放电容量、能量、电流需再乘以并数a。
在一种可能的实施例方式中，所述电池异常自放电预警方法，还包括：
在所述工况信息为放电状态、所述一致性为理想化状态且所述结构信息为多并多串的情况下，获取两次充电结束的第四时间间隔；
获取两次充电结束后的单体荷电状态极值；
基于前一次充电结束后的单体最低电压和单体最高电压获取第四极差值；
通过后一次充电结束后的单体最低电压和单体最高电压获取第五极差值；
将所述第四极差值和所述第五极差值之间的差值作为第六极差值；
根据所述第六极值差、单体额定容量、单体额定能量和所述第四时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
在一种可能的应用场景中，第一次放电结束时Vmax1≠Vmin1，相应的SOC高1≠SOC低1。如果在第一次放电结束之后，第二次放电结束之前，电池发生了自放电问题，则Vmax2＞Vmin2，相应的SOC高2＝SOC低2。则在这两次放电结束之间的时间段内，自放电容量为((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1，自放电能量为((SOC低1-SOC低2)-(SOC高1-SOC高2))*e1，平均自放电电流为((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1/△t1。需要说明的是，在本实施例中第四极差值为(SOC低1-SOC低2)，第五极差值为(SOC高1-SOC高2)，x1为单体额定容量，e1为单体额定能量，△t1为第五时间间隔。
在一些实施方式中，以放电之后静置一段时间去极化之后的数据做分析效果更佳。
在一种可能的实施例方式中，所述电池异常自放电预警方法，还包括：
在所述工况信息为静置状态且静置时长大于或等于预设静置时长阈值的情况下，获大于所述预设静止时长阈值对应时间段中两次静置状态下的第五时间间隔；
获取两次静置状态下的单体荷电状态极值；
基于前一次静置状态下的单体最低电压和单体最高电压获取第七极差值；
通过后一次静置状态下的单体最低电压和单体最高电压获取第八极差值；
将所述第八极差值和所述第七极差值之间的差值作为第九极差值；
根据所述第九极值差、单体额定容量、单体额定能量和所述第五时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
在一种可能的应用场景中，静置场景的选取和静置数据的应用有两个方面的优势，第一：车辆下电、没有任何电耗，创造了一种类实验室自放电检测的工况。第二：车辆低功耗静置，电流≤1/20C(电池系统1小时满放电的电流称为1C)，是一种较为理想的消除电池极化的场景，可以提高电池自放电计算的准确度。
车辆低功耗静置(如电流≤1/20C)较长一段时间，那么取静置t1(为较好的去极化一般t1需≥10分钟，十分钟预设静置时长阈值)时刻的最高单体电压Vmax1、最低单体电压Vmin1，对应的荷电状态为SOC高1、SOC低1；取静置结束前一时刻t2的最高单体电压Vmax2、最低单体电压Vmin2，对应的荷电状态为SOC高2、SOC低2。
假如在此静置时间段内某电芯有自放电问题，则其自放电容量为((SOC高2-SOC低2)-(SOC高1-SOC低1))*x1，自放电能量为((SOC高2-SOC低2)-(SOC高1-SOC低1))*e1，平均自放电电流为((SOC高2-SOC低2)-(SOC高1-SOC低1))*x1/(t2-t1)。需要说明的是，在本实施例中第八极差值即为(SOC 高2-SOC低2)，第七极差值即为(SOC高1-SOC低1))，第九极差值为((SOC高 2-SOC低2)-(SOC高1-SOC低1))，x1为单体额定容量，e1为单体额定能量，(t2-t1)为第五时间间隔。
在一种可能的实施例方式中，所述电池异常自放电预警方法，还包括：
步骤S500：获取所述车辆不同使用年限、不同充电倍率和不同无功耗搁置时长的单体电压极值的电压压降幅度或电压恢复程度，基于所述电压压降幅度或电压恢复程度，判断所述车辆电池系统是否出现自放电。
在一种可能应用场景中，车辆从大倍率充电工况下突然下电，进入无功耗状态，过一段时间t1后，车辆重新上电。车辆下电前的最高单体电压是Vmax1，最低单体电压是Vmin1；车辆重新上电后的最高单体电压是Vmax2、最低单体电压是Vmin2。由于车辆下电前大倍率充电，电池有极化，车辆静置一段时间后去极化，所以Vmax1＞Vmax2，Vmin1＞Vmin2。基于大数据，统计不同使用年限(循环次数)、不同充电倍率、不同无功耗搁置时长(去极化时长)的最高电压单体、最低电压单体的电压压降幅度，如果发现某电池系统的某次静置后最低电压的压降程度明显变大，则说明此电芯有自放电问题。
举例1，大数据统计一万台车辆在使用2年后的电池、循环周期为100周、使其在1C充电倍率时下电进入无功耗状态，静置60分钟后，统计最高单体电压下降a伏特，最低单体电压下降b伏特。但是，某同样的电池同样的充电、静置工况，最高单体电压下降a伏特，最低单体电压下降(b+c)伏特，则说明最低电压单体有自放电问题。查OCV-SOC数据表得出电压降c伏特对应的SOC，则自放电容量为SOC*x1，自放电能力为SOC*e1，自放电电流为SOC*x1/60分钟。
举例2，大数据统计车辆的某套电池系统已使用2年、循环周期为100周，使其在1C充电倍率时下电进入无功耗状态，静置60分钟后最高单体电压下降a伏特，最低单体电压下降b伏特。但是，某次相同工况静置60分钟之后，最高单体电压下降a伏特，最低单体电压下降(b+c)伏特。说明此最低电压单体有自放电问题，查OCV-SOC数据表得出电压降c伏特对应的SOC，则自放电容量为SOC*x1，自放电能力为SOC*e1，自放电电流为SOC*x1/60分钟。
在另一种可能的应用场景中，车辆从大倍率放电工况下突然下电，进入无功耗状态，过一段时间t1后，车辆重新上电。车辆下电前的最高单体电压是Vmax1，最低单体电压是Vmin1；车辆重新上电后的最高单体电压是Vmax2、最低单体电压是Vmin2。由于车辆下电前大倍率放电，电池有极化，车辆静置一段时间后去极化，所以Vmax1＜Vmax2，Vmin1＜Vmin2。基于大数据统计，如果下电静置时间短，所有电芯的电压恢复(升高)程度基本一致，下电静置时间越长某电芯恢复电压程度越小，甚至为负值，则可以说明此电芯有明显 自放电问题。
在另一种可能的应用场景中，车辆以任意工况进入下电无功耗状态，然后定时唤醒监控，一般唤醒几分钟，唤醒状态时功耗非常小，通常情况下电流只有零点几安培，可以当做无功耗状态看待。此种情况类似实验室对电池的自放电进行检测。每隔一段时间检测一下最高单体电压、最低单体电压，分析压降趋势。理论上，所有单体电池均有微弱的自放电。基于经验，短时间内，例如24小时以内，最高单体电压维持不变，如果电池有自放电问题，最低单体电压降呈现明显下降趋势。以此判断电池的异常自放电问题，并计算相应的自放电容量、自放电能量、自放电电流。
举例：某电池下电后以时间间隔a小时自唤醒(a在通常情况下大于1)，每次唤醒时长b小时(b相对a非常小)，第一次唤醒时最高单体电压Vmax1、最低单体电压Vmin1，对应的电池荷电状态是SOC高1、SOC低1，此时由于已经静置了a小时，可以认为电池已经较好的去极化。第二次唤醒时最高单体电压Vmax2、最低单体电压Vmin2，对应的电池荷电状态是SOC高2、SOC低2。则在时间a小时内，电池的自放电容量是((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1，自放电能量是((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1，平均自放电电流是((SOC低1-SOC低2)-(SOC高1-SOC高2))*x1/a。由于Vmax1≈Vmax2，所以SOC高1≈SOC高2，所以以上公式可以简化为电池的自放电容量是(SOC低1-SOC低2)*x1，自放电能量是(SOC低1-SOC低2)*x1，平均自放电电流是(SOC低1-SOC低2)*x1/a。
还存在一种可能的应用场景中，基于表显SOC计算单位SOC变化充入电池系统的电量变化规律。行业一般做法，为尽量避免车辆SOC出现跳变问题，表显SOC在电池使用过程中会进行中断跟随、高端修正、低端修正。随着电池系统中的某个或某几个电芯出现异常自放电问题后，电池系统的一致性会恶化。表显SOC从0到100的满充、满放电量、能力将降低。考虑到电池系统在短时间内不会有明显的衰减问题，如果短时间内表显SOC对应的充电电量或放电电量有明显的变小趋势，说明电池出现了明显的自放电问题。
此种方法应发挥云平台的大数据优势，对所有同型号、相同使用年限、相同使用循环次数、相同SOH的车辆和电池进行航向对比分析，求出平均的单位SOC变化对应的充电电量或放电电量。一般情况下，BMS会向云平台上报充电电量。云平台也可以进行安时积分计算充电电量或放电电量。
在一些可能的实时方式中，也可纵向对比某台车在历史上的单位SOC对应的充电电量或放电电量，如果在短时间(比如3个月内)发生了明显的变小，也可以判断此车的电池出现了明显的自放电问题。
假如某车在观察到的第一次充电过程单位SOC(1％SOC)对应的充电电量(容量)是1Ah(假定额定容量是100Ah，额定能量是e)，在观察 到的第2次充电过程中单位SOC(1％SOC)对应的充电电量(容量)是0。9Ah，两次充电时间间隔是t1，则判断此时间段内发生了电池自放电问题，自放电电量(容量)是(1-0.9)*100＝10Ah，自放电能量是(1-0.9)*100e，自放电电流是(1-0.9)*100/t1＝10/t1。
在一种可能实施例中，还可以基于电压列表数据进行自放电问题检测，电压列表可以是有厂商上传到云平台的数据，可以基于所述各实施例中的分析方法，基于电压列表数据对每个单体电芯的自放电情况进行分布计算，识别自放电电流最大的电芯，进行提前预警，哪怕它还不是电压最低的电芯。
举例1：对应所述第十三种情况，基于电压列表：车辆从大倍率放电工况下突然下电，进入无功耗状态，过一段时间t1后，车辆重新上电。车辆下电前的最高单体电压是Vmax1，最低单体电压是Vmin1；车辆重新上电后的最高单体电压是Vmax2、最低单体电压是Vmin2。由于车辆下电前大倍率放电，电池有极化，车辆静置一段时间后去极化，所以Vmax1＜Vmax2，Vmin1＜Vmin2。如果大部分电芯的恢复电压是a，某个或某几个单体电芯的恢复电压明显比a小，则说明这几个电芯有自放电问题或其极化电压非常小(等效于极化内阻非常小)。对比所述对自放电的识别，此方法可以进一步佐证电芯有自放电问题。
举例2：对应所述第四种情况：第一次充电结束之后的所有单体电压为V11/V12/V13……V1N，平均单体电压是V1mea，对应的电池荷电状态分别是SOC11/SOC12/SOC13…。SOC1N/SOC1mea；第二次充电结束之后所有单体电压为V21/V22/V23……V2N，平均单体电压是V2mea，对应的电池荷电状态分别是SOC21/SOC22/SOC23…。SOC2N/SOC2mea；两次充电时间间隔是t1,以平均电芯电压为参考对象，分别计算对应的自放电电流。举例第一个电芯的自放电容量为((SOC11-SOC21)-(SOC1mea-SOC2mea))*x1，自放电能量为((SOC11-SOC21)-(SOC1mea-SOC2mea))*e1，平均自放电电流为((SOC11-SOC21)-(SOC1mea-SOC2mea))*x1/t1。
在一种可能的实施例方式中，还可以基于电芯温度传感器对目标车辆电池的自放电问题进行判断和热失控风险评估，其中，理论上，车辆静置状态时，外界大气温度稳定、均匀时，电池的温度会趋近于气温，电池因为充放电、热管理系统不均匀带来的温度差异会趋于变小。但是，如果某电芯有异常自放电问题，放出的电量会转化为热量，提高此电芯的温度。以此作为电芯自放电问题预警的辅助判断手段，同时根据自放电电流大小、温度大小作为电池发生热失控风险大小的评价方法，自放电电流越大、静置温度越高，电池热失控风险越大。此种方法，要求电池系统具备每个电芯上有温度传感器，以提高温度识别的精度。
本公开通过从车辆电池系统的不同工况状态、不同电池结构和不同一致性信息，计算车辆电池系统的自放电容量、自放电能量和平均自放电电 流值，并基于自放电容量、自放电能量和/或平均自放电电流值判断车辆电池系统是否出现自放电，从而实现了在不同工况、不同电池结构以及不同所有单体电芯一致性状态下，车辆电池系统自放电情况的预警，进而大大的提高了车辆电池系统的安全性，以及乘驾人员的用户体验。
请参阅图2，图2为本公开实施例提供的一种电池异常自放电预警系统结构图示意图，如图2所示，一种电池异常自放电预警系统，包括状态获取模块100、数据计算模块200、系统判断模块300和安全预警模块400，其中：
状态获取模块100，用于基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；数据计算模块200，用于基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；系统判断模块300，用于根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；安全预警模块400，用于基于所述判断结果进行电池安全预警。
可以理解的是，本公开提供的一种电池异常自放电预警系统与前述各实施例提供的电池异常自放电预警方法相对应，电池异常自放电预警系统的相关技术特征可参考电池异常自放电预警方法的相关技术特征，在此不再赘述。
请参阅图3，图3为本公开实施例提供的电子设备的实施例示意图。如图3所示，本公开实施例提供了一种电子设备，包括存储器1310、处理器1320及存储在存储器1310上并可在处理器1320上运行的计算机程序1311，处理器1320执行计算机程序1311时实现以下步骤：
基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；基于所述判断结果进行电池安全预警。
请参阅图4，图4为本公开提供的一种计算机可读存储介质的实施例示意图。如图4所示，本实施例提供了一种计算机可读存储介质1400，其上存储有计算机程序1411，该计算机程序1411被处理器执行时实现如下步骤：
基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻 数据时间间隔；基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；根据所述自放电容量、所述自放电能量和所述平均自放电电流值判断所述车辆电池系统是否出现自放电；基于所述判断结果进行电池安全预警。
本公开提供的一种电池异常自放电预警方法、系统、电子设备及存储介质，方法包括：基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；基于所述判断结果进行电池安全预警。本公开通过基于车辆电池系统的工况信息、结构信息和一致性信息，计算车辆电池系统的自放电容量、自放电能量和平均自放电电流值，并基于自放电容量、自放电能量和/或平均自放电电流值判断车辆电池系统是否出现自放电，从而实现了在不同工况、不同电池结构以及不同所有单体电芯一致性状态下，车辆电池系统自放电情况的预警，进而大大的提高了车辆电池系统的安全性，以及乘驾人员的用户体验。
需要说明的是，在所述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详细描述的部分，可以参见其它实施例的相关描述。
本领域内的技术人员应明白，本公开的实施例可提供为方法、系统、或计算机程序产品。因此，本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本公开是参照根据本公开实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式计算机或者其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个 流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本公开的优选实施例，但本领域内的技术人员一旦得知了基本创造概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本公开范围的所有变更和修改。
显然，本领域的技术人员可以对本公开进行各种改动和变型而不脱离本公开的精神和范围。这样，倘若本公开的这些修改和变型属于本公开权利要求及其等同技术的范围之内，则本公开也意图包括这些改动和变型在内。

Claims (10)

  1. 一种电池异常自放电预警方法，包括：
    基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；
    基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；
    根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；
    基于所述判断结果进行电池安全预警。
  2. 根据权利要求1所述的电池异常自放电预警方法，所述基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值的步骤，包括：
    在所述工况信息为充电结束状态、所述一致性信息为理想化状态以及所述结构信息为单个电芯组成电芯组且各个电芯组之间串联或多个电芯组成电芯组且各个电芯组之间串联的情况下，获取两次充电结束的第一时间间隔，其中，所述理想化状态为前一次充电结束后的单体最低电压和单体最高电压之间的差值小于或等于预设差值；
    获取后一次充电结束后的单体荷电状态极值，其中，所述单体荷电状态极值包括单体荷电状态极大值和单体荷电状态极小值；
    基于所述后一次充电结束后单体荷电状态极大值和单体荷电状态极小值获取第一极值差；
    根据所述第一极值差、单体额定容量、单体额定能量和所述第一时间间隔计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
  3. 根据权利要求2所述的电池异常自放电预警方法，还包括：
    在所述工况信息为充电结束状态、所述结构信息为单个电芯组成电芯组且各个电芯组之间串联和所述一致性信息为非理想化状态的情况下，获取两次充电结束的第二时间间隔，其中，所述非理想化状态为前一次充电结束后的单体最低电压和单体最高电压之间的差值大于预设差值；
    获取前一次充电结束后和后一次充电结束后单体荷电状态极小值的差值为第二极差值；
    根据所述第二极值差、单体额定容量、单体额定能量和所述第二时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
  4. 根据权利要求2所述的电池异常自放电预警方法，还包括：
    在所述工况信息为放电状态、所述一致性信息为理想化状态且所述结构信息为单并多串的情况下，获取两次充电结束的第三时间间隔；
    获取后一次充电结束后的单体荷电状态极值；
    基于所述后一次充电结束后单体荷电状态极大值和单体荷电状态极小值获取第三极值差；
    根据所述第三极值差、单体额定容量、单体额定能量和所述第三时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
  5. 根据权利要求4所述的电池异常自放电预警方法，还包括：
    在所述工况信息为放电状态、所述一致性为理想化状态且所述结构信息为多并多串的情况下，获取两次充电结束的第四时间间隔；
    获取两次充电结束后的单体荷电状态极值；
    基于前一次充电结束后的单体最低电压和单体最高电压获取第四极差值；
    通过后一次充电结束后的单体最低电压和单体最高电压获取第五极差值；
    将所述第四极差值和所述第五极差值之间的差值作为第六极差值；
    根据所述第六极值差、单体额定容量、单体额定能量和所述第四时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
  6. 根据权利要求2所述的电池异常自放电预警方法，还包括：
    在所述工况信息为静置状态且静置时长大于或等于预设静置时长阈值的情况下，获取大于所述预设静止时长阈值对应时间段中两次静置状态之间的第五时间间隔；
    获取两次静置状态下的单体荷电状态极值；
    基于前一次静置状态下的单体最低电压和单体最高电压获取第七极差值；
    通过后一次静置状态下的单体最低电压和单体最高电压获取第八极差值；
    将所述第八极差值和所述第七极差值之间的差值作为第九极差值；
    根据所述第九极值差、单体额定容量、单体额定能量和所述第五时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值。
  7. 根据权利要求1所述的电池异常自放电预警方法，还包括：
    获取所述车辆不同使用年限、不同充电倍率和不同无功耗搁置时长的单体电压极值的电压压降幅度或电压恢复程度，基于所述电压压降幅度或电压恢复程度，判断所述车辆电池系统是否出现自放电。
  8. 一种电池异常自放电预警系统，包括：
    状态获取模块，用于基于车辆电池系统的工况信息、结构信息和一致性信息，获取所述车辆电池系统当前工况周期内多个单体电压极值、单体荷电状态极值和相邻数据时间间隔；
    数据计算模块，用于基于所述单体电压极值、所述单体荷电状态极值和所述相邻数据时间间隔，计算所述车辆电池系统的自放电容量、自放电能量和平均自放电电流值；
    系统判断模块，用于根据所述自放电容量、所述自放电能量和所述平均自放电电流值中的任一项是否大于预设阈值以判断所述车辆电池系统是否出现自放电；
    安全预警模块，用于基于所述判断结果进行电池安全预警。
  9. 一种电子设备，其特征在于，包括存储器、处理器，所述处理器用于执行存储器中存储的计算机管理类程序时实现如权利要求1-7任一项所述的电池异常自放电预警方法的步骤。
  10. 一种计算机可读存储介质，其特征在于，其上存储有计算机管理类程序，所述计算机管理类程序被处理器执行时实现如权利要求1-7任一项所述的电池异常自放电预警方法的步骤。
PCT/CN2023/081831 2022-09-21 2023-03-16 电池异常自放电预警方法、系统、电子设备及存储介质 WO2024060537A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP23764216.0A EP4369013A1 (en) 2022-09-21 2023-03-16 Method and system for early warning of abnormal self-discharge of battery, and electronic device and storage medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202211154244.6A CN115616410A (zh) 2022-09-21 2022-09-21 电池异常自放电预警方法、系统、电子设备及存储介质
CN202211154244.6 2022-09-21

Publications (1)

Publication Number Publication Date
WO2024060537A1 true WO2024060537A1 (zh) 2024-03-28

Family

ID=84859269

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2023/081831 WO2024060537A1 (zh) 2022-09-21 2023-03-16 电池异常自放电预警方法、系统、电子设备及存储介质

Country Status (3)

Country Link
EP (1) EP4369013A1 (zh)
CN (1) CN115616410A (zh)
WO (1) WO2024060537A1 (zh)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115616410A (zh) * 2022-09-21 2023-01-17 岚图汽车科技有限公司 电池异常自放电预警方法、系统、电子设备及存储介质
CN116008820B (zh) * 2023-03-24 2023-10-10 中国汽车技术研究中心有限公司 针对车辆电池单体不一致性的检测方法、设备和介质
CN116736153B (zh) * 2023-08-15 2023-11-21 宁德时代新能源科技股份有限公司 过放电预警方法、装置、电子设备、存储介质和程序产品
CN117590264A (zh) * 2023-11-29 2024-02-23 湖南银杏电池智能管理技术有限公司 一种电池系统静置电流自适应计算方法、设备及介质
CN117341477B (zh) * 2023-12-06 2024-04-02 浙江凌骁能源科技有限公司 磷酸铁锂动力电池内短路预警方法、装置和计算机设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111751737A (zh) * 2020-07-07 2020-10-09 安徽江淮汽车集团股份有限公司 动力电池的自放电电流计算方法、装置、设备及存储介质
CN111873852A (zh) * 2020-07-01 2020-11-03 广州小鹏汽车科技有限公司 一种动力电池自放电监测方法及装置、车辆、存储介质
CN112327180A (zh) * 2019-11-27 2021-02-05 万向一二三股份公司 一种锂离子电池自放电评价及其电压均衡调节方法
CN115061051A (zh) * 2022-06-10 2022-09-16 重庆金康赛力斯新能源汽车设计院有限公司 一种电池电压异常的识别方法、装置、计算机设备和介质
CN115616410A (zh) * 2022-09-21 2023-01-17 岚图汽车科技有限公司 电池异常自放电预警方法、系统、电子设备及存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112327180A (zh) * 2019-11-27 2021-02-05 万向一二三股份公司 一种锂离子电池自放电评价及其电压均衡调节方法
CN111873852A (zh) * 2020-07-01 2020-11-03 广州小鹏汽车科技有限公司 一种动力电池自放电监测方法及装置、车辆、存储介质
CN111751737A (zh) * 2020-07-07 2020-10-09 安徽江淮汽车集团股份有限公司 动力电池的自放电电流计算方法、装置、设备及存储介质
CN115061051A (zh) * 2022-06-10 2022-09-16 重庆金康赛力斯新能源汽车设计院有限公司 一种电池电压异常的识别方法、装置、计算机设备和介质
CN115616410A (zh) * 2022-09-21 2023-01-17 岚图汽车科技有限公司 电池异常自放电预警方法、系统、电子设备及存储介质

Also Published As

Publication number Publication date
CN115616410A (zh) 2023-01-17
EP4369013A1 (en) 2024-05-15

Similar Documents

Publication Publication Date Title
WO2024060537A1 (zh) 电池异常自放电预警方法、系统、电子设备及存储介质
US11215679B2 (en) Method and apparatus for detecting micro short circuit of battery
US10393819B2 (en) Method and apparatus for estimating state of battery
CN108152755B (zh) 在线定量诊断电池微短路故障的方法
CN109507611B (zh) 一种电动汽车的soh修正方法及系统
CN103424710A (zh) 用于监测电池组中的老化单体的性能变化的方法和系统
WO2008065910A1 (en) Accumulator failure detecting device, accumulator failure detecting method, accumulator failure detecting program, and computer-readable recording medium containing the accumulator failure detecting program
JP5568583B2 (ja) リチウムイオン二次電池システム、リチウムイオン二次電池の検査方法、リチウムイオン二次電池の制御方法
KR20140053590A (ko) 배터리 잔존 용량 추정 장치 및 방법
CA2899239A1 (en) Method for determining a state of charge and remaining operation life of a battery
JP7463008B2 (ja) 電池セル診断装置および方法
JPWO2012137456A1 (ja) 余寿命判定方法
JP6970289B2 (ja) 充電制御装置、輸送機器、及びプログラム
CN103823191A (zh) 一种计算锂离子电池组可用剩余容量的方法
CN114158276B (zh) 检测镀锂的方法和通过使用其来管理电池的方法和装置
JP7326237B2 (ja) 複数の電池に関する判定装置、蓄電システム、判定方法及び判定プログラム
CN114552039A (zh) 一种电池常带电自保养的控制方法及常带电自保养的电池
JP2000270491A (ja) リチウムイオン電池充電方法及びリチウムイオン電池充電装置
CN112415399B (zh) 电池单体ocv-soc曲线修正方法、设备及存储介质
CN113740754B (zh) 一种检测电池组不一致性的方法与系统
WO2023035160A1 (zh) 动力电池充电的方法和电池管理系统
CN112421721B (zh) 电动车电源管理方法、装置、设备及存储介质
KR20220012747A (ko) 배터리를 진단하기 위한 장치 및 그 방법
US20150212163A1 (en) Device for detecting remaining battery capacity, battery system, method of detecting remaining battery capacity and program
CN117347888B (zh) 电池均衡仪载荷测量方法、装置、计算机设备及存储介质

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2023764216

Country of ref document: EP

Effective date: 20230911